Rapid Probe Engagement and Withdrawal with Force Minimization in Atomic Force Microscopy: A Learning-Based Online-Searching Approach

In this article, the problem of rapid probe engagement and withdrawal in atomic force microscopy (AFM) is addressed. Probe engagement and withdrawal is needed in almost all AFM operations, ranging from imaging to nanomanipulation. However, due to the highly nonlinear force-distance relation, large probe-sample interaction force can be induced during the probe engagement and withdrawal process, resulting in sample deformation and damage and measurement errors. Rapid probe engagement and withdrawal is needed to achieve high-speed AFM operations, particularly, to capture and interrogate dynamic evolutions of the sample. We propose an online-searching-based optimization approach to minimize both the engagement (and withdrawal) time and the interaction force. The force-displacement profile of the probe is partitioned and then optimized sequentially, by immersing optimal trajectory design and iterative learning control into the Fibonacci search process. The proposed approach is illustrated through experimental implementations on two different types of polymer species, a polydimethylsiloxane sample, and a dental silicone sample, respectively.